Vu, T.M., Buckley, C., Bai, H. et al. (6 more authors) (2020) Multiobjective genetic programming can improve the explanatory capabilities of mechanism-based models of social systems. Complexity, 2020. 8923197. ISSN 1076-2787
Abstract
The generative approach to social science, in which agent-based simulations (or other complex systems models) are executed to reproduce a known social phenomenon, is an important tool for realist explanation. However, a generative model, when suitably calibrated and validated using empirical data, represents just one viable candidate set of entities and mechanisms. The model only partially addresses the needs of an abductive reasoning process - specifically it does not provide insight into other viable sets of entities or mechanisms, nor suggest which of these are fundamentally constitutive for the phenomenon to exist. In this paper, we propose a new model discovery framework that more fully captures the needs of realist explanation. The framework exploits the implicit ontology of an existing human-built generative model to propose and test a plurality of new candidate model structures. Genetic programming is used to automate this search process. A multi-objective approach is used, which enables multiple perspectives on the value of any particular generative model - such as goodness-of-fit, parsimony, and interpretability - to be represented simultaneously. We demonstrate this new framework using a complex systems modeling case study of change and stasis in societal alcohol use patterns in the US over the period 1980-2010. The framework is successful in identifying three competing explanations of these alcohol use patterns, using novel integrations of social role theory not previously considered by the human modeler. Practitioners in complex systems modeling should use model discovery to improve the explanatory utility of the generative approach to realist social science.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Tuong M. Vu et al. This is an open access article distributed under the Creative Commons Attribution License, (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Health and Related Research (Sheffield) > ScHARR - Sheffield Centre for Health and Related Research |
Funding Information: | Funder Grant number NATIONAL INSTITUTES OF HEALTH - UNITED STATES OF AMERICA 1R01AA024443-01A1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 06 Apr 2020 13:03 |
Last Modified: | 07 Jul 2020 15:08 |
Status: | Published |
Publisher: | Hindawi |
Refereed: | Yes |
Identification Number: | 10.1155/2020/8923197 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:159212 |